Browsing by Author "Mathew, Ezek"
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Item An analysis of an aberrant circumflex artery originating from the right aortic sinus and its clinical implications.(2019-03-05) Mobeen, Misha; Mathew, Ezek; Reeves, Rustin; Nguyen, Minh-Triet (Michael)During routine cadaver dissection, a coronary vessel abnormality was discovered in a 71-year old female cadaver. The circumflex artery (CxA), normally a branch from the left coronary artery (LCA), took its origin from the root of the right coronary artery (RCA) instead. It appears to exit the right aortic sinus sharing the same coronary ostium as the RCA. The vessel veered left, taking a retroaortic course between the aorta and left ventricle towards the left side of the heart. Although the female donor’s death was due to chronic hypoxemia and respiratory failure secondary to chronic obstructive pulmonary disease, there was no medical history evidence of pathological conditions due to the variant coronary vessel. The aberrant CxA is rare due to its origin from the RCA. In addition, the normal perfusion area for the CxA appears markedly reduced in this case, possibly due to constriction as it loops posterior to the aorta. Preliminary measurement of the CxA indicated some possible sites of constriction, and overall the diameter of the vessel was small. Furthermore, the RCA and LCA perfusion areas appeared to compensate for the reduction. Surgical case reports implicate the significance of a CxA arising from the right coronary sinus. One such clinical significance for this variant would be an increased difficulty with aortic valve replacement due to the origination site of the CxA.Item Branched-chain amino acids are neuroprotective against traumatic brain injury and enhance rate of recovery: prophylactic role for contact sports and emergent use(2022) Mathew, Ezek; Williamson, Julie; Mahama-Rodriguez, Alia; Mamo, Lois; Dickerman, RobBackground: Branched-chain amino acids (BCAA) are known to be neurorestorative after traumatic brain injury (TBI). Despite clinically significant improvements in severe TBI patients given BCAA, the approach is largely an unrecognized option. Furthermore, TBI continues to be the most common cause of morbidity and mortality in adolescents and adults. In this study, we sought to demonstrate the neuroprotective and restorative effects of BCAA on the sequelae of TBI. No study has evaluated whether BCAA can be preventive or neuroprotective if taken before a TBI. We hypothesized that if BCAA were elevated in the circulation prior to TBI, the brain would readily access the BCAA and the severity of injury could be reduced. Methods: A standard weight-drop method was used on 50 adult mice to model a closed-head TBI in humans. The mice were randomized into groups that were shams, untreated, and pre-treated with BCAA, post-treated with BCAA, or pre-treated + post-treated with BCAA. Pretreated mice received BCAA through supplemented water and were dosed via oral gavage 45 mins prior to TBI induction. All mice underwent beam walking to assess motor recovery and Morris water maze assessed cognitive function post-injury. Results: Pre-treated and pre-treated + post-treated mice exhibited significantly better motor recovery and cognitive function than the other groups. The pre-treated + post-treated group performed the best overall while the post-treated group only improved in memory after day 7 of the study. Conclusion: This is the first study, animal or human, to demonstrate BCAA are neuroprotective and neurorestorative after TBI, most likely through the important roles of BCAA to glutamate homeostasis.Item Characterization of rHDL Nanoparticles as a Delivery Vehicle for Glioblastoma Multiforme Chemotherapy(2021) Mathew, Ezek; Sabnis, Nirupama; Dossou, Akpedje; Dickerman, Rob; Lacko, Andras G.; Fudala, RafalPurpose: Glioblastoma Multiforme (GBM), is a brain tumor that presents with a very poor prognosis; new approaches are needed to improve patient outcomes. Design of an effective therapeutic approach must include a suitable delivery vehicle, which can cross the blood-brain barrier, and can selectively target GBM tumors. Our project uses reconstituted high-density lipoprotein (rHDL) nanoparticles (NPs), which possess the above characteristics, amplifying efficacy of chemotherapy. To target the PI3K/mTOR pathway involved in the pathophysiology of GBM, we chose to encapsulate PI-103 in preliminary studies. Methods: After encapsulation and purification, the drug-containing rHDL NPs will be characterized with regard to their physical/chemical properties. We will assess drug loading, entrapment efficiency, stability, particle diameter, homogeneity and molecular weight. Afterwards, cytotoxicity against human GBM cells will be compared to normal human astrocytes. Because the scavenger receptor B type 1 (SR-B1) is known to interact with circulating HDL and the rHDL NPs, we will compare the cytotoxic efficiency of the drug transporting rHDL NPs against a high SR-B1 expressing GBM line (LN229) versus a low SR-B1 expressing GBM line (U87MG). SR-B1 levels will be assessed for all cell lines. Results: In this work we will demonstrate that after encapsulation of PI-103 into rHDL and characterization, we will observe cytotoxic effect against GBM cell lines, not normal astrocytes. Conclusion: If successful, future spheroid and mice studies, in addition to combination therapy studies, will advance the proof of concept of this therapy, allowing translation toward clinical applications.Item Development of a Machine Learning Model to Design Target-specific Ligands(2022) Mathew, Ezek; Liu, Jin; Wang, Duen-Shian; Liu, KevinBackground: As the estimated cost required to bring a drug to market ranges from $314 million to $2.8 billion, drug discovery is undoubtedly a lengthy and expensive process. Additionally, completion of Phase 3 trials does not guarantee FDA approval. For most drugs, the probability of receiving FDA approval ranges from 9% to 14%, depending on the time period. Therefore, researchers have turned to machine learning (ML) to decrease the burden of drug discovery for multiple targets. In the central nervous system (CNS), the metabotropic glutamate receptor subtype 2 (mGlu2) and metabotropic glutamate receptor subtype 3 (mGlu3) play various roles in normal physiology. Therefore, ligands of these receptors pose potential for the treatment of various pathologies, such as Alzheimer's disease, schizophrenia, and other neurological disorders. Currently, no literature exists referencing a machine learning model that is capable of distinguishing drug ligands based on their affinity to mGlu2 or mGlu3. To fill this gap in knowledge, we will design a machine learning algorithm capable of making associations across the entire data set, identifying patterns that the human eye cannot detect. Methods: We utilized a dataset which included two dimensional (2D) images of drug ligands belonging to two classes, mGlu2 or mGlu3. The images were resized, then converted into grayscale and subsequently processed as a numerical NumPy array with their associated labels. Convolutional Neural Network (CNN) and Functional API architecture were tested to determine the optimal model. Hyperparameter optimization occurred throughout this process. Results: The CNN and Functional API both reached 100% accuracy within 20 epochs, successfully classifying ligands as mGlu2 or mGlu3 based on 2D structure alone. However, the Functional API reached 100% accuracy in under 5 epochs, yielding superior performance when compared to the CNN. Conclusion: While the CNN is one of the most popular ML architectures for image classification, the Functional API can perform a similar role. As datasets expand, it may be beneficial to consider more efficient models, especially for image classification in the realm of drug discovery.Item Identification of New Allosteric Modulators for the mGlu2 Receptor by using a Ligand-based Drug Discovery Approach(2023) Nguyen, Trong; Kumari, Pratibha; Mathew, Ezek; Liu, JinPurpose: The human mGlu receptors are G protein-coupled receptors located within the central nervous system. These receptors normally bind to glutamate, which is the primary excitatory neurotransmitter in the body. The receptors can then assist in modulating the transmission of excitatory signals within the brain. These characteristics help to make the mGlu2 receptor a potential, novel target for future drug development, particularly for the treatment of certain neurologic or neuropsychiatric disorders, such as schizophrenia or depression. However, most allosteric ligands bind non-selectively on both mGlu2 and mGlu3 receptors. A pharmacological tool that assists with distinguishing ligands specific to mGlu2 and mGlu3 receptor subtypes will be pivotal to speed-up the drug discovery process. Our purpose in this study is to find novel ligands of potential allosteric modulators for the mGlu2 receptor by using already identified modulators through a ligand-based drug designing approach. Methods: The potential allosteric ligands for the mGlu2 receptor were obtained by performing similarity searches on the online databases, ZINC and Drugbank. The original compounds used as the basis for the similarity searches came from a previously compiled list of Top 39 ZINC mGlu2 ligands (from the Liu Lab). Once the ligands were downloaded, they were converted into the appropriate file formats for molecular docking. Due to time constraints, it was decided that we would only dock the compounds whose original ligands had <10 results obtained from similar searching through ZINC. The selected ligands were then docked using Autodock Vina and visualized using Pymol. The Top 3 ligands were then determined based on their presence within the mGlu2 allosteric binding pocket and their predicted binding affinity for the receptor. Additionally, these ligands were also analyzed using a previously developed machine learning model. Specifically, the machine learning model would predict mGlu2 ligand likeness and binding affinity for each of the obtained ligands. Results: A total of 1507 allosteric ligands were obtained for the mGlu2 receptor through the similarity searches. Machine learning model analysis of the similar ligands deemed that 88.89% of them were more likely to be mGlu2 ligands. Additionally, 83.50% of the ligands were deemed to have a high predicted binding affinity for the mGlu2 receptor. A total of 46 compounds were docked to the mGlu2 receptor using Autodock Vina, and their predicted binding affinities were obtained. The Top 3 similar ligands for the mGlu2 receptor, listed in order, exhibited binding affinities of -12.5 kcal/mol, -12.3 kcal/mol and -11.0 kcal/mol. Conclusion: We were able to identify 1507 potential ligands for the mGlu2 receptor through similarity searches. Through further molecular docking of 46 of the similar ligands, we have determined three specific allosteric ligands for the mGlu2 receptor that are comparable or slightly better to their original counterparts. However, we believe additional research and investigation is required for validation of their potential efficacy. Future studies should involve analysis of the specific protein-ligand interactions that exist between the mGlu2 receptor and the three similar allosteric ligands, followed by comparison with the interactions present in their original counterparts.Item In-Vitro Assessment of Cortical Repair Induced by Branched-chain Amino Acid Treatment(2024-03-21) Mathew, Ezek; Jones, Nathan; Dickerman, Rob; Ortega, SterlingPurpose: Traumatic Brain Injury (TBI) refers to a constellation of pathologies resulting from mechanical damage to cortical tissue. The neurological sequala of such injuries can be devastating, and definitive treatment does not exist at this time. Branched-chain Amino Acid (BCAA) treatment has demonstrated neuroprotective effects in clinical literature and in various animal models of TBI. However, there is a lack of in-vitro literature referencing the repair capacity of BCAA administration after neuronal injury, particularly in the context of TBI. To fill this gap in knowledge, a scratch assay was repurposed for use in cortical culture, to assess the repair capacity of BCAA treatment. Methods: Mouse-derived Mixed Cortical Culture (MCC) cells were extracted and seeded in 24 well plates. A scratch assay was performed, where a vertical scratch was drawn across each well in a reproducible manner with a 200 uL pipette tip. This procedure is meant to recapitulate aspects of mechanical damage induced by TBI on cortical tissue. Subsequently, images were taken immediately post-injury (0 hour time point), at 24 hour, and at 48 hour time points post-scratch to quantify the area of scratch unfilled by cells. Various dose concentrations of BCAA were tested in comparison to control (media only) and vehicle control (water). Test conditions included the customary BCAA ratio, which is a 2:1:1 mix of leucine, isoleucine, and valine; additionally, a 1:1:1 ratio of leucine, isoleucine, and valine was also tested. Results: At 48 hours post-scratch, significant differences were found in open wound area when comparing the media only control to 10 uM (p < 0.01), 30 uM (p < 0.01), 300 uM (p < 0.001), and 1000 uM (p < 0.01) of the 2:1:1 BCAA dose. Significant differences were also found in the wound area when comparing the water vehicle control to 10 uM (p < 0.05), 30 uM (p < 0.01), 300 uM (p < 0.01), and 1000 uM (p < 0.01) of the 2:1:1 BCAA dose. No significant differences were found in the open wound area when comparing the controls and BCAA doses, at the 24 hour time point. Of note, no significant differences were found between control and treatment with the 1:1:1 ratio of leucine, isoleucine, and valine at any time point. Conclusion: BCAA treatment at the 2:1:1 ratio was seen to accelerate injury recovery at various dose concentrations, as quantified by open wound area after scratch injury was induced. This cell culture model demonstrates the importance of BCAA ratios. While this aligns with animal models and clinical literature, this is the first in-vitro assessment of BCAA repair capacity, in the context of cortical culture. Future studies will be undertaken to further elucidate the constituents of the repair mechanism.Item Left Hemi-Diaphragmatic Paralysis After Left Cervical Transforaminal Epidural Steroid Injection of the C5-C6 Level(2020) Mathew, Ezek; Dickerman, Rob; Farrell, MollyBackground: Cervical radiculopathy is a common cause of neck pain with radiation into the upper extremity in a dermatomal pattern. The age-adjusted incidence is 83.2 per 100,000 persons per year. The most common causes are vertebral spondylosis and intervertebral disc herniation. Corticosteroid injection is a conservative management option with a low risk of major adverse events. Adverse events could include epidural hematoma, infection, allergic reactions, seizures, nerve damage, or intravascular injections. No reviewed literature or case reports have indicated phrenic nerve injury secondary to cervical transforaminal epidural steroid injection (TFESI). Case Presentation: A 45-year-old male physician with severe left C6 radiculopathy secondary to a large left-sided C5-C6 herniated intervertebral disc presented to the neurosurgical clinic. The patient underwent a left side C6 TFESI. Immediately upon awakening from anesthesia, the patient experienced shortness of breath. A Sniff test demonstrated the patient had left diaphragmatic paralysis. Six weeks later, the patient underwent a C5-C6 anterior cervical discectomy and fusion with complete relief of his radicular symptoms. The left hemi-diaphragmatic paralysis remained at the one-year postoperative visit. Conclusion: A thorough literature review shows no indication of phrenic nerve injury with cervical TFESI. In the current study, we explore the suspected mechanisms of possible injury to the phrenic nerve. Epidural corticosteroid injection is a viable and safe option for conservative management of cervical radiculopathy. This report unveils a unique and important adverse event that should be held in consideration before undergoing a cervical TFESI.Item Leveraging Graph Attention Mechanisms to Create an Explainable Multi-Function Machine Learning Model(2024-03-21) Mathew, Ezek; Madugula, Sita Sirisha; Emmitte, Kyle; Liu, JinPurpose: Identifying target-specific ligands is a difficult task, especially in cases where receptors display high structural similarity. Such is the case for metabotropic glutamate receptor subtype 2 (mGlu2) and metabotropic glutamate receptor subtype 3 (mGlu3), which are prime targets for various neurological treatments. However, signal transduction through these two receptors often yields opposing physiological function and differentially affect pathologies. Methods: Understanding the need to differentiate ligands based on their binding to mGlu2 and mGlu3, we employed a machine learning (ML) approach. The ML model performed three distinct tasks and leveraged transfer learning to inform each subsequent task. Task 1: Simple Classification was performed, as the ML model predicted if the ligands displayed selectivity for the mGlu2 or mGlu3 class. Task 2: Regression was performed, as the ML model estimated the IC50 values of individual input ligands. The classification weights from Task 1 were broadcasted into the attention layers of the ML model for Task 2, serving as a starting point. Task 3: Classification was performed, as the ML model sought to determine if a ligand displayed low or high potency for the target class. Classification weights and regression weights from previous tasks were broadcasted into the model. Results: The model yielded greater than 99% accuracy in the selectivity classification task, while also delivering satisfactory performance when predicting potency (72.80% error). The model yielded 83% accuracy in correctly identifying high potency mGlu2 ligands, as high potency mGlu2 compounds. Meanwhile, the algorithm displayed 75% accuracy in correctly identifying high potency mGlu3 ligands, as high potency mGlu3 compounds. Conclusions: This approach allows for prediction of multiple target properties using a single model. With access to other high-quality datasets, this model has the potential to apply to other ligand classes of interest, posing great potential for drug repurposing studies.Item Prediction of Ligand Selectivity and Efficacy Using Artificial Intelligence Algorithms(2023) Mathew, Ezek; Wang, Duen-Shian; Liu, Kevin; Pham, Tyler; Madugula, Sita Sirisha; Emmitte, Kyle; Liu, JinPurpose: Identifying target-specific ligands is extremely challenging in drug discovery, especially in cases where receptors display high structural similarity. Such is the case for metabotropic glutamate receptor subtype 2 (mGlu2) and metabotropic glutamate receptor subtype 3 (mGlu3), which are prime targets for various neurological treatments. However, signal transduction through these two receptors often yields opposing physiological function and differentially affects pathologies. The purpose of this study is to develop artificial intelligence (AI) methods to predict ligand selectivity and efficacy on similar targets. Methods: Understanding the need to differentiate ligands based on their binding to mGlu2 and mGlu3, we employed a machine learning approach. Using patent-derived datasets, data was pre-processed into an eight-dimension vector space. Afterwards, the data was flattened, and a Multiple Input and Output (MIO) Model was designed to receive the incoming vectors. A classification arm was designated as an output, differentiating input structures as mGlu2 or mGlu3 ligands. In addition, this novel MIO Model with Functional application program interface (API) architecture also has the capacity to estimate efficacy of an input ligand by outputting Half-maximal inhibitory concentration (IC50) value. Results: The model yielded greater than 96% accuracy in the classification task to predict the binding selectivity of the ligands, while simultaneously delivering satisfactory performance when predicting efficacy. With regards to the regression arm, the model attained about 81% accuracy in correctly identifying high-affinity mGlu2 compounds, and 62% accuracy in correctly identifying high-affinity mGlu3 compounds. We then used docking studies, and the trained model to screen an available ZINC database, selecting the top 39 compounds out of 9270 ligands. Conclusions: This approach can pave the way for computer aided searches which screen for high efficacy ligands belonging to a certain class of interest. More specifically, this model can be used in combination with other established structure-based methodology like molecular docking, allowing for screening of even more drug candidates for further study and validation. With access to other high-quality datasets, this model has the potential to apply to other ligand classes of interest, posing great potential for drug repurposing studies.Item Prediction of Ligand Selectivity and Efficacy Using Artificial Intelligence Algorithms(2024-03-21) Yeung, Tatiana; Mathew, Ezek; Liu, Kevin; Madugula, Sirisha; Nguyen, Trong; Pham, Tyler; Liu, JinIntroduction: Bringing new pharmaceuticals to market is a time-intensive and expensive process. The purpose of this project is to combine computational structure based approaches such as docking and machine learning methodologies to yield ideal pharmaceutical candidates for future exploration. The ligands of interest were those that bind to the Dopamine 4 (D4) and Sigma 1 (S1) receptors, serving as prime candidates for treatment of neurological ailments. Design of more efficacious and selective ligands could allow researchers and clinicians to improve treatment of patients with such conditions. The primary objective is to leverage advancements in computational chemistry to approach the problem of identifying ideal drug candidates using both ligand based and structure-based approaches. Methods: Receptor structures were identified for both the D4 receptor using the PDB 5WIU, and the S1 receptor using the PDB 5HK1. A list of possible ligands was obtained from a DrugBank database, collaborators at the University of Nebraska Medical Center, and a similarity search. The DrugBank database of FDA approved drugs was scanned for ligands to both the D4 and S1 receptors, and a list of 1415 drug ligands was compiled. Using Autodock software, we docked each ligand with 10 poses. After docking, the ligands were ranked by binding affinity. Using Autodock software, the 83 ligands we received from our collaborators were also docked and ranked by binding affinity to the D4 and S1 receptors. To further expand our pool of potential candidates for further study, a similarity search was conducted by screening through a drug database (ZINC) to identify the FDA approved drugs that were most structurally similar to the 83 ligands. Ligands from both the DrugBank and the similarity search were integrated into a machine learning pipeline using graph neural networks to predict the Ki values, thereby identifying compounds with high binding affinity. Once the ligands of highest affinity are identified by the machine learning model, they will be sent to our collaborators for in vitro testing. Results: Of the top 50/1450 FDA approved drugs with the lowest Ki values for both D4, 18 overlapped with the top 50/1450 lowest Ki values for S1. Docking the 83 ligands to the D4 and S1 receptors showed that the ligands were generally more strongly bonded to S1 than D4. We will deliver the top ligand candidates belonging to both D4 and S1 ligand classes as identified by the machine learning model to our collaborators so they can perform further in vitro testing. This will allow us to validate our computational efforts with real world testing. Conclusion: We will leverage the trained machine learning model to search through more databases and identify other prime candidates for future exploration.Item Subjective memory complaints and cardiovascular risk factors: a cross-sectional study of the HABS-HD cohort(2022) Mathew, Ezek; Vintimilla, Raul; Hall, James; Johnson, Leigh; O'Bryant, SidBackground: Subjective memory complaints (SMC) are considered as subjects' interpretation of their cognitive aspects, such as memory and perception. Cardiovascular risk factors such as hypertension, diabetes, dyslipidemia, and obesity may contribute to cognitive decline and their relationship with dementia has been documented extensively. However, there is a lack of literature on the relationship between CVRFs and SMC. Depression has been linked to cardiovascular disease and it is strongly associated with SMC, so it is important to consider the contribution of CVRFs and depression as potentially modifiable factors of SMC. Despite the importance of SMC as a risk factor for cognitive decline, and the higher burden of CVRFs, cognitive decline and dementia among minorities like Mexican Americans (MA), not much attention has been paid to the study of SMC in this population. This study examined the factors associated with SMC in community-dwelling older MA and non - Hispanic Whites (NHW), particularly CVRFs and depression. We hypothesized that CVRFs will be associated with SMC, and that the association will be independent of depression. Methods: We studied 1,376 cognitively normal participants (673 MA and 673 NHW) from the Health and Aging Brain Study (HABS - HD). Baseline characteristics were analyzed using t and chi square tests. The presence of SMC was ascertained by the Subjective Memory Complaints Questionnaire (SCMQ). A logistic regression was conducted to examine the relationship of subjective memory complaints with CVRFs and depression. Age, gender, and education were entered as covariates in the model. Results: MA with SMC had a higher prevalence of dyslipidemia (p=0.008), and depression (p< 0.0001) than those without SMC. Fifty nine percent of the NHW sample were female. NHW with SMC were less educated than those without SMC (mean education years 15.26 vs 15.83), and have a higher prevalence of diabetes (p=0.04) and depression (p< 0.0001). When comparing baseline characteristics of MA (323) and NHW (269) with SMC, we found that MA were younger (mean age 63.74 vs 68.85) and less educated (mean education years 9.38 vs 15.26). MA with SMC had a higher prevalence of diabetes (p< 0.0001) and obesity (p=0.0001) when compared with NHW with SMC. Depression was strongly associated with SMC in MA (OR 3.46; 95% CI = 2.45 - 4.89) and NHW (OR 2.22; 95% CI = 1.59 - 3.10). Dyslipidemia was also associated with SMC in MA (OR 1.73; 95% CI = 1.25 - 2.40). NHW with less education had an increased likelihood of exhibiting SMC. Conclusions: Our findings suggest that the association of CVRF and SMC differs among MA and NHW. Depression was strongly associated with SMC in both groups. In MA, dyslipidemia was also associated with SMC in MA, while education was a significant factor only in NHW. The complex relationship between memory complains, vascular risk factors, and depression requires longitudinal studies for further clarification. Understanding SMC and its racial differences may allow early interventions to prevent cognitive decline.Item Transverse Myelitis After Johnson & Johnson COVID Vaccine - A Case Report(2022) Mathew, Ezek; Williamson, Julie; Johnson, Reign; Mamo, Lois; Mahama-Rodriguez, Alia; Dickerman, RobIntroduction: As the novel coronavirus disease of 2019 (COVID) is an ongoing public health issue, many turn to vaccinations as a means of defense. While vaccination is generally safe, reports of rare pathologies subsequent to COVID vaccination exist, especially in the realm of neurological disorders. One such rare complication is tranverse myelitis, which will be the subject of this case report. Patients impacted by transverse myelitis may present with a varied neurological symptom, which may sometimes progress rapidly without treatment. These can include motor, sensory, and/or autonomic dysfunctions stemming from the spinal cord. These dysfunctions typically occur bilaterally at clearly defined sensory levels, and T2 weighted MRI will indicate cord hyperintensity. Case Description: A 56-year-old male patient presented to clinic with a chief complaint of episodic bilateral arm numbness. The patient tested positive for COVID in December of 2020, although recovery was uneventful. In May of 2021, the patient received the Johnson & Johnson COVID vaccine. The symptoms associated with his chief complaint developed approximately two months after receiving the vaccine. Two weeks preceding the patient visit, cervical Magnetic Resonance Imaging (MRI) was performed. Imaging evidenced severe cord edema from C1 to T1-2 with associated cord expansion. At C4-C5, there is a right sided disc protrusion causing moderate spinal stenosis with cord effacement. Additionally, the thecal sac measures 7mm at this level. At the C5-C6 and C6-C7 levels, there is evidence of moderate foraminal stenosis, bilaterally. Radiological evaluation confirmed these findings, while listing possible differentials of transverse myelitis, neuromyelitis optica, or a viral myelitis. Along with recommendation for follow up and referral for contrast MRI, oral corticosteroid treatment was rapidly initiated. One week after treatment, another cervical MRI was performed. The radiology interpretation noted decreased extent of the abnormal enhancing signal within the cervical cord, compatible with resolving transverse myelitis. Over the time course of multiple weeks, symptoms improved. Discussion: While the majority of cases may yield abnormal strength and DTR, transverse myelitis presentations after COVID vaccination may ultimately vary widely, necessitating thorough evaluation. The prognosis of transverse myelitis is rather varied and depends on factors such as rate of symptom progression, quality of nerve conduction, possibility of spinal shock, and speed of treatment initiation. Prompt treatment and management of symptoms may allow for a successful recovery, as in this patient's case.Item Unilateral to Bilateral Progressive Sciatic Neuropathy After Radiotherapy: A Case Report(2024-03-21) Mathew, Ezek; Drown, Mariah; Abarquez, Angela; Shivnani, Anand; Ortega, Sterling; Dickerman, RobBackground: Radiation therapy is often an adjunct treatment for prostate cancer. However, this procedure is not without risks; as the lumbosacral plexus is not routinely contoured during radiotherapy treatment plans, this raises potential for unintended consequences. As this case, especially this particular presentation, is an extremely rare occurrence, we will examine relevant literature and discuss the challenging diagnosis. Case Presentation: In this report, we detail the case of a 66-year-old male patient who suffered from unilateral sciatic neuropathy. Unfortunately, this unilateral neuropathy became bilateral, and was deemed idiopathic at the time, causing the patient severe distress. However, further workup which consisted of examination of patient history, scrutinizing imaging, and electromyography (EMG), painted a different picture. The onset of the patient’s complaints appeared to be initiated by adaptive radiotherapy, which the patient underwent during his treatment regimen for prostate cancer. Conclusions: As radiation-induced lumbosacral plexopathy (RILSP) may present in a delayed fashion after treatment, diagnosis could become difficult. While radiculopathy was the differential diagnosis which initially led to neurosurgical consultation, the patient’s presentation did not align with this diagnosis. Further workup, especially strategic usage of EMG, allowed for discernment of a neuropathic condition, versus a mechanically induced radiculopathy. While RILSP appears to be an underreported phenomenon subsequent to pelvic radiation, there exists only one other case of such neuropathy after prostate radiotherapy. Knowledge of this case will enable clinicians to modify their workup and avoid spine surgery in cases where it may cause harm.